On Multifractal Structure in Non-Representational Art

Multifractal analysis techniques are applied to patterns in several abstract expressionist artworks, paintined by various artists. The analysis is carried out on two distinct types of structures: the physical patterns formed by a specific color (``blobs''), as well as patterns formed by the luminance gradient between adjacent colors (``edges''). It is found that the analysis method applied to ``blobs'' cannot distinguish between artists of the same movement, yielding a multifractal spectrum of dimensions between about 1.5-1.8. The method can distinguish between different types of images, however, as demonstrated by studying a radically different type of art. The data suggests that the ``edge'' method can distinguish between artists in the same movement, and is proposed to represent a toy model of visual discrimination. A ``fractal reconstruction'' analysis technique is also applied to the images, in order to determine whether or not a specific signature can be extracted which might serve as a type of fingerprint for the movement. However, these results are vague and no direct conclusions may be drawn.Comment: 53 pp LaTeX, 10 figures (ps/eps

Computational expressionism : a study of drawing with computation

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 1999.Includes bibliographical references (leaves 68-73).This thesis presents computational expressionism, an exploration of drawing using a computer that redefines the concepts of line and composition for the digital medium. It examines the artistic process involved in computational drawing, addressing the issues of skill, algorithmic style, authorship, re-appropriation, interactivity, dynamism, and the creative/evaluative process. The computational line augments the traditional concept of line making as a direct deposit or a scratching on a surface. Digital representation is based on computation; appearance is procedurally determined. The computational line embodies not only an algorithmic construction, but also dynamic and interactive behavior. A computer allows us to construct drawing instruments that take advantage of the dynamism, interactivity, behavioral elements and other features of a programming environment. Drawing becomes a two-fold process, at two distinct levels of interaction with the computer. The artist has to program the appearance and behavior of lines and subsequently draw with these lines by dragging a mouse or gesturing with some other input device. The compositions incorporate the beauty of computation with the creative impetus of the hand, whose apparent mistakes, hesitations and inspirations form a complex and critical component of visual expression.by Joanna Maria Berzowska.S.M

Vera Molnar's computer paintings

The Hungarian-born French painter Vera Molnar is one of the few artists who pioneered the use of the computer as a creative medium starting in the late 1960s. This article explores how Molnar’s computer-generated works used programming as a means to reflect upon the autographicity of the handmade trace in drawing and painting.</jats:p

Robotic arts: Current practices, potentials, and implications

Given that the origin of the “robot” comes from efforts to create a worker to help people, there has been relatively little research on making a robot for non-work purposes. However, some researchers have explored robotic arts since Leonardo da Vinci. Many questions can be posed regarding the potentials of robotic arts: (1) Is there anything we can call machine-creativity? (2) Can robots improvise artworks on the fly? and (3) Can art robots pass the Turing test? To ponder these questions and see the current status quo of robotic arts, the present paper surveys the contributions of robotics in diverse forms of arts, including drawing, theater, music, and dance. The present paper describes selective projects in each genre, core procedure, possibilities and limitations within the aesthetic computing framework. Then, the paper discusses implications of these robotic arts in terms of both robot research and art research, followed by conclusions including answers to the questions posed at the outset

Can Computers Create Art?

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.Comment: to appear in Arts, special issue on Machine as Artist (21st Century

Convolutional neural networks for style classification

Amb la col·laboració d'aquestes universitats: UNIVERSITAT DE BARCELONA UNIVERSITAT ROVIRA I VIRGILIIn recent years convolutional neural networks have enjoyed great success. Especially in the field of object recognition great leaps forward have been made. Researchers were able to exploit the object detection features from such networks for many useful and interesting applications like sentiment analysis and information retrieval. Unfortunately, many times the importance of style is not being considered adequately in these systems. This is partly because style is a concept that is difficult to define and labeled data is scarce. Recent developments in texture synthesis and style transfer, however, sparked new interest in the field. In particular feature correlations from convolutional neural networks, which were trained on object recognition, have been shown to work well on these tasks. I propose that such techniques can help in classifying style. In the course of this thesis I setup a experiment to show that this is indeed the case. Furthermore, I show that the performance of the CNN and the depth of the layer from which the feature correlations are taken from influences the classification performance

Fine-grained painting classification

A lot of progress has been made in the domain of image classification in the deep learning era, however, not so much for paintings. Even though paintings are images they are very different from photographs and classification of paintings requires in-depth domain knowledge compared to classifying an object. This makes the task of fine-grained classification of paintings even harder. In this thesis, we evaluate the classification of paintings into its various styles, genres, artists and formulate the problem of dating paintings as a classification problem. We experiment with the standard networks available as baselines and then improve the classification models via multi-task learning. We also propose a novel architectural addition to the VGG network to do fine-grained classification. Our models beat the existing state-of-the-art classifiers by a big margin

Machinic Eyes: New and Post-Digital Aesthetics, Surveillance, and Resistance

This work concerns the rise of the New Aesthetic, an art project developed by James Bridle in 2012. The New Aesthetic, as envisioned by Bridle, was chiefly concerned with the overlapping of physical and digital realities through both the artifacts produced by this overlapping and the systems involved therein. I introduce the advent of the New Aesthetic and present the major criticisms: the lack of a robust theoretical and scholarly framework, the lack of a historical framework, the privileging of artifacts over systems as new Aesthetic, and the fragmented scholarly outlook on the New Aesthetic. Upon further examination, I discovered that the New Aesthetic is less of an art project but a metaphor for a global surveillance apparatus that is the result of clandestine partnerships between multinational technology corporations and intelligence agencies associated the Five Eyes consortium. In this dissertation, I critique the New Aesthetic from a scholarly viewpoint, offer a historical precedent of how the New Aesthetic came to be from cultural and technological perspectives, examine the rise of the global surveillance apparatus within the New Aesthetic, and offer ideas of how to resist surveillance as a result of our reliance upon computational technologies

High-Tech Trash

High-Tech Trash analyzes creative strategies in glitch, noise, and error to chart the development of an aesthetic paradigm rooted in failure. Carolyn L. Kane explores how technologically influenced creative practices, primarily from the second half of the twentieth and first quarter of the twenty-first centuries, critically offset a broader culture of pervasive risk and discontent. In so doing, she questions how we continue onward, striving to do better and acquire more, despite inevitable disappointment. High-Tech Trash speaks to a paradox in contemporary society in which failure is disavowed yet necessary for technological innovation.  “Leonard Cohen sang ‘There’s a crack in everything…that’s how the light gets in.’ Here, Carolyn Kane teaches us how to see that light, one crack at a time.” FRED TURNER, author of The Democratic Surround: Multimedia and American Liberalism from World War II to the Psychedelic Sixties  “Kane profiles art practices and media discourses that exploit and celebrate, rather than filter or suppress, all kinds of errors and noises. A welcome intervention in a number of discursive fields.” PETER KRAPP, author of Noise Channels: Glitch and Error in Digital Culture  “An original work of scholarship that addresses some of the most pervasive phenomena and foundational questions in the contemporary media environment.” ROBERT HARIMAN, coauthor of The Public Image: Photography and Civic Spectatorship  CAROLYN L. KANE is Associate Professor of Communication at Ryerson University and author of Chromatic Algorithms: Synthetic Color, Computer Art, and Aesthetics after Code